From: Leonid Gibiansky <*LGibiansky*>

Date: Thu, 16 Apr 2009 08:22:01 -0400

Mats,

Another difference between BLOCK(2) and DIAG(3) is that they provide

different number of ETAs for the individual fit. I am a bit surprised

that one-compartment model with random effects on CL, V, and F is

identifiable (even with diagonal OMEGA). Indeed, for each subject, this

model has 3 free parameters. The only thing that allows to identify them

separately is the distributional assumption. It could be rather week so

I would expect higher variance values with DIAG(3) versus BLOCK(2).

How often have you used ETAs on CL, V, and F in the same one-compartment

model (without IV arm)? Is it always stable (or at least as stable as

BLOCK(2))?

Thanks

Leonid

--------------------------------------

Leonid Gibiansky, Ph.D.

President, QuantPharm LLC

web: www.quantpharm.com

e-mail: LGibiansky at quantpharm.com

tel: (301) 767 5566

Mats Karlsson wrote:

*> Hi Steve,
*

*>
*

*> For a one-compartment model I think these are differences:
*

*>
*

*> 1) DIAG(3) is more restrictive than BLOCK(2) in the sense that only positive
*

*> correlation between CL/F and V/F can be estimated
*

*> 2) DIAG(3) is less restrictive than BLOCK(2) in the sense that different
*

*> transformations can be used for F
*

*> 3) DIAG(3) provides an EBE that can be used for diagnostic purposes (DIAG(3)
*

*> and BLOCK(2) would give the same estimates for the same model so I don't
*

*> understand your comment of var(F) being higher than cov(CL/F,V/F))
*

*> 4) DIAG(3) may facilitate covariate model building (although this is minor
*

*> as you with BLOCK(2) can put the same relationship in in two places)
*

*> 5) If there truly is a mixture in F1, then I think DIAG(3) has a advantages
*

*> over BLOCK(2) in number of parameters (two fewer) needed to describe the
*

*> variability model
*

*> 6) If some additional assumptions can be reliably made, such as all
*

*> variability in F1 is truly in bioavailability and bioavailability is
*

*> restricted to be between 0 and 1, some additional info may be extracted from
*

*> the data for example by .
*

*>
*

*> I would not rank any of these as major differences (expect possibly the
*

*> mixture aspect which I've never tried).
*

*>
*

*> For two- or three-compartment models the advantages are that if indeed the
*

*> main covariance structure between CL/F, V1/F, Q/F, V2/F is a joint positive
*

*> correlation due to variability in bioavailability, fu etc, then a DIAG(5) is
*

*> more parsimonious than a BLOCK(4).
*

*>
*

*> Mats
*

*>
*

*> Mats Karlsson, PhD
*

*> Professor of Pharmacometrics
*

*> Dept of Pharmaceutical Biosciences
*

*> Uppsala University
*

*> Box 591
*

*> 751 24 Uppsala Sweden
*

*> phone: +46 18 4714105
*

*> fax: +46 18 471 4003
*

*>
*

*>
*

*> -----Original Message-----
*

*> From: Stephen Duffull [mailto:stephen.duffull *

*> Sent: Thursday, April 16, 2009 10:13 AM
*

*> To: Mats Karlsson; drmould *

*> nmusers *

*> Subject: RE: [NMusers] OMEGA BLOCK with mixture model?
*

*>
*

*> Mats
*

*>
*

*>> With oral data only I would normally model with BLOCK(2) on
*

*>> CL/F and V/F or a DIAG(3) on CL/F, V/F and relative F. The
*

*>> latter may have some advantages for diagnostics, covariate
*

*>> model building etc.
*

*>
*

*> I have often seen these two options considered. I am unclear as to the
*

*> advantages of DIAG(3) over BLOCK(2)? In theory it would seem that they
*

*> should be identical. In practice it seems that DIAG(3) is more relaxed
*

*> since it is not required that the variance of relative F if reassigned to
*

*> the covariance of (CL/F, V/F) [under BLOCK(2)] yields a positive definite
*

*> matrix.
*

*>
*

*> I presume an advantage wrt covariate model building would be access to the
*

*> EBEs of F_i. However, given the variance of F_i may exceed the covariance
*

*> of (CL/F, V/F) then I wonder if this is a real advantage or an artefact of
*

*> numerical procedures?
*

*>
*

*> I am keen to learn more about real advantages of application of DIAG(3) as
*

*> an alternative to BLOCK(2).
*

*>
*

*> Steve
*

*> --
*

*> Professor Stephen Duffull
*

*> Chair of Clinical Pharmacy
*

*> School of Pharmacy
*

*> University of Otago
*

*> PO Box 913 Dunedin
*

*> New Zealand
*

*> E: stephen.duffull *

*> P: +64 3 479 5044
*

*> F: +64 3 479 7034
*

*>
*

*> Design software: www.winpopt.com
*

*>
*

*> *

Received on Thu Apr 16 2009 - 08:22:01 EDT

Date: Thu, 16 Apr 2009 08:22:01 -0400

Mats,

Another difference between BLOCK(2) and DIAG(3) is that they provide

different number of ETAs for the individual fit. I am a bit surprised

that one-compartment model with random effects on CL, V, and F is

identifiable (even with diagonal OMEGA). Indeed, for each subject, this

model has 3 free parameters. The only thing that allows to identify them

separately is the distributional assumption. It could be rather week so

I would expect higher variance values with DIAG(3) versus BLOCK(2).

How often have you used ETAs on CL, V, and F in the same one-compartment

model (without IV arm)? Is it always stable (or at least as stable as

BLOCK(2))?

Thanks

Leonid

--------------------------------------

Leonid Gibiansky, Ph.D.

President, QuantPharm LLC

web: www.quantpharm.com

e-mail: LGibiansky at quantpharm.com

tel: (301) 767 5566

Mats Karlsson wrote:

Received on Thu Apr 16 2009 - 08:22:01 EDT